Yka Huhtala

TANE: An Efficient Algorithm for Discovering Functional and Approximate Dependencies

The discovery of functional dependencies from relations is an important database analysis technique. We present TANE, an efficient algorithm for finding functional dependencies from large databases. TANE is based on partitioning the set of rows with respect to their attribute values, which makes testing the validity of functional dependencies fast even for a large number of tuples. The use of partitions also makes the discovery of approximate functional dependencies easy and efficient and the erroneous or exceptional rows can be identified easily. Experiments show that T ANE is fast in practice. For benchmark databases the running times are improved by several orders of magnitude over previously published results. The algorithm is also applicable to much larger datasets than the previous methods.

Efficient discovery of functional and approximate dependencies using partitions

Discovery of functional dependencies from relations has been identified as an important database analysis technique. We present a new approach for finding functional dependencies from large databases, based on partitioning the set of rows with respect to their attribute values. The use of partitions makes the discovery of approximate functional dependencies easy and efficient, and the erroneous or exceptional rows can be identified easily. Experiments show that the new algorithm is efficient in practice. For benchmark databases the running times are improved by several orders of magnitude over previously published results. The algorithm is also applicable to much larger datasets than the previous methods.

Tracking Outdoor Sports --- User Experience Perspective

In this paper, the potential role of a sport tracking application is examined in the context of supporting tracking outdoor sporting activities. A user study with 28 participants was conducted to study the usage habits and user experiences evoked. The application consists of a mobile tracking tool and a related web service. It collects and stores workout data such as the route, speed and time, and compiles a training diary that can be viewed in many ways during the exercise and afterwards. Data can be uploaded into a web service for further analysis or for sharing it with others. The results show high interest in tracking outdoor sports with a mobile phone application --- the participants used the application during almost all exercise sessions and stated that they would continue using the application after the study. Sharing data was not perceived as valuable, although some usage scenarios for social sharing arose.

Mining for similarities in aligned time series using wavelets

Discovery of non-obvious relationships between time series is an important problem in many domains, such as financial, sensory, and scientific data analysis. We consider data mining in aligned time series, which arise, e.g., in numerous online monitoring applications, and we are interested in finding time series which reflect the same external events. The time series can have different vertical positions, scales and overall trends, however still show related features at the same locations. The features can be short-term, such as small peaks and turns, or long-term, such as wider mountains and valleys. We propose using a wavelet transformation of a time series to produce a natural set of features for the sequence. Wavelet transformation yields features which describe properties of the sequence, both at various locations and at varying time granularities. In the proposed method, these features are processed so that they are insensitive to changes in the vertical position, scaling, and overall trend of the time series. We discuss the use of these features in data mining, in tasks such as clustering. We demonstrate how the features allow a flexible analysis of different aspects of the similarity: we show how one can examine how the similarity between time series changes as a function of time or as a function of time granularity considered. We present experimental results with real financial data sets. Experiments indicate that the proposed method can produce useful results. For instance, important similarities can be found in time series, which would be considered unrelated by visual inspection. Experiments with compression give encouraging results for the application of the method in mining massive time series data sets.